@article{bibcite_36787,
  keywords = {Affordable and Clean Energy, AI training, Artificial Intelligence, Benchmark testing, Bioengineering, Computational modeling, Engineering, GPU power measurements, Graphics processing units, Hardware, Information and Computing Sciences, Large Language Models, Networking and Information Technology R\&D, Power demand, Power measurement, Stress, sustainable computing, Technology, Training, Visualization},
  author = {Imran Latif and Alex C Newkirk and Matthew R Carbone and Arslan Munir and Yuewei Lin and Jonathan G Koomey and Xi Yu and Zhihua Dong},
  title = {Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System},
  abstract = {<p>The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying the energy footprint of this infrastructure requires models parameterized by the power demand of AI hardware during training. In this work, we measured the instantaneous power draw of an 8-GPU NVIDIA H100 HGX node during the training of open-source image classifier (ResNet) and large-language models (Llama2-13b). We characterize power demand for a single node configuration, providing foundational data for future multi-node studies. The maximum observed power draw was approximately 8.4 kW, 18\% lower than the manufacturer-rated 10.2 kW, even with GPUs near full utilization. Holding model architecture constant, increasing batch size from 512 to 4096 images for ResNet reduced total training energy consumption by a factor of 4. These findings can inform capacity planning for data center operators and energy use estimates by researchers. Future work will investigate the impact of cooling technology and carbon-aware scheduling on AI workload energy consumption.</p>},
  year = {2025},
  booktitle = {IEEE Access},
  journal = {IEEE Access},
  series = {IEEE Access},
  volume = {13},
  pages = {61740-61747},
  month = {26/03/2025},
  institution = {Institute of Electrical and Electronics Engineers (IEEE)},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  issn = {2169-3536},
  url = {https://doi.org/10.1109/access.2025.3554728},
  doi = {10.1109/access.2025.3554728},
}